Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high-latitude  version model (ORC-HL-PEAT)

Abstract. Widely present in boreal regions, peatlands contain large carbon stocks because of their hydrologic properties and high water content, which makes primary productivity exceed decomposition rates. We have enhanced the global land surface model ORCHIDEE by introducing a hydrological representation of northern peatlands. These peatlands are represented as a new plant functional type (PFT) in the model, with specific hydrological properties for peat soil. In this paper, we focus on the representation of the hydrology of northern peatlands and on the evaluation of the hydrological impact of this implementation. A prescribed map based on the inventory of Yu et al. (2010) defines peatlands as a fraction of a grid cell represented as a PFT comparable to C3 grasses, with adaptations to reproduce shallow roots and higher photosynthesis stress. The treatment of peatland hydrology differs from that of other vegetation types by the fact that runoff from other soil types is partially directed towards the peatlands (instead of directly to the river network). The evaluation of this implementation was carried out at different spatial and temporal scales, from site evaluation to larger scales such as the watershed scale and the scale of all northern latitudes. The simulated net ecosystem exchanges agree with observations from three FLUXNET sites. Water table positions were generally close to observations, with some exceptions in winter. Compared to other soils, the simulated peat soils have a reduced seasonal variability in water storage. The seasonal cycle of the simulated extent of inundated peatlands is compared to flooded area as estimated from satellite observations. The model is able to represent more than 89.5 % of the flooded areas located in peatland areas, where the modelled extent of inundated peatlands reaches 0.83×106 km2. However, the extent of peatlands in northern latitudes is too small to substantially impact the large-scale terrestrial water storage north of 45∘ N. Therefore, the inclusion of peatlands has a weak impact on the simulated river discharge rates in boreal regions.

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Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high latitude version model (ORC-HL-PEAT)

10.5194/gmd-2017-141 ◽

2017 ◽

Cited By ~ 1

Author(s):

Chloé Largeron ◽

Gerhard Krinner ◽

Philippe Ciais ◽

Claire Brutel-Vuilmet

Keyword(s):

Land Surface ◽

Peat Soil ◽

Water Storage ◽

Land Surface Model ◽

Model Description ◽

High Water Content ◽

Surface Model ◽

Northern Latitudes ◽

Global Land ◽

Northern Peatlands

Abstract. Widely present in boreal regions, peatlands contain large carbon stocks because of their hydrologic properties and high water content, making decomposition smaller than primary productivity. We have enhanced the global land surface model ORCHIDEE by introducing northern peatlands. These are considered as a new Plant Functional Types (PFT) in the model, with specific hydrological properties for peat soil. In this paper, we focus on the representation of the hydrology of northern peatlands and on the evaluation of the hydrological impact of this implementation. A prescribed map based on the inventory of Yu et al. (2010) defines peatlands as a fraction of grid cell represented as a PFT comparable to C3 grasses with adaptations to reproduce shallow roots and higher photosynthesis stress. The treatment of peatland hydrology differs from that of other vegetation types by the fact that runoff from other soil types is partially directed towards the peatlands (instead of directly to the river network). The evaluation of this implementation was carried out according to different spatial and temporal scales, from site evaluation to larger scales such as watershed scale and all northern latitudes scale. The simulated net ecosystem exchanges are in agreement with observations from 3 FLUXNET sites. Water table positions were generally close to observations, with some exceptions in winter. Compared to other soils, the simulated peat soil have a reduced seasonal variability of water storage. The seasonal cycle of inundated peatlands area is also compared to flooded area estimated from satellite observations. The model is able to represents more than 89.5 % of the flooded areas located in peatlands areas, where the modelled extent of inundated peatlands reaches 0.83 Mkm2. However, the extent of peatland in northern latitudes is small enough that is does not impact the terrestrial water storage at scale of latitudes over 45° N. Therefore, the inclusion of peatlands has a weak impact on the river discharge located in boreal regions.

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Executive Editor Comment on "Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high latitude version model"

10.5194/gmd-2017-141-sc1 ◽

2017 ◽

Author(s):

Astrid Kerkweg

Keyword(s):

High Latitude ◽

Land Surface ◽

Land Surface Model ◽

Model Description ◽

Surface Model ◽

Editor Comment ◽

Executive Editor ◽

Global Land ◽

Northern Peatlands

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Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

10.5194/egusphere-egu21-13546 ◽

2021 ◽

Author(s):

Jonathan Barichivich ◽

Philippe Peylin ◽

Valérie Daux ◽

Camille Risi ◽

Jina Jeong ◽

...

Keyword(s):

Tree Ring ◽

Land Surface ◽

Forest Ecosystems ◽

Ring Width ◽

Land Surface Model ◽

Surface Model ◽

Growth Variability ◽

Tree Ring Data ◽

Surface Models ◽

Global Land

Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (&#916;13C) and oxygen (&#948;18O) stable isotopes in six sites in boreal and temperate Europe.&#160; The model simulates &#916;13C (r = 0.31-0.80) and &#948;18O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with &#948;18O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, &#916;13C and &#948;18O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.

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Modelling northern peatlands area and carbon dynamics since the Holocene with the ORCHIDEE-PEAT land surface model (SVN r5488)

10.5194/gmd-2018-256 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chunjing Qiu ◽

Dan Zhu ◽

Philippe Ciais ◽

Bertrand Guenet ◽

Shushi Peng ◽

...

Keyword(s):

Carbon Storage ◽

Land Surface ◽

Net Primary Production ◽

Soil Column ◽

Land Surface Model ◽

Surface Model ◽

Past Century ◽

Positive Trend ◽

Peat Core ◽

Northern Peatlands

Abstract. The importance of northern peatlands in the global carbon cycle has recently been recognized, especially for long-term changes. Yet, the complex interactions between climate and peatland hydrology, carbon storage and area dynamics make it challenging to represent these systems in land surface models. This study describes how peatland are included as an independent sub-grid hydrological soil unit (HSU) into the ORCHIDEE-MICT land surface model. The peatland soil column in this tile is characterized by multi-layered vertical water and carbon transport, and peat-specific hydrological properties. A cost-efficient TOPMODEL approach is implemented to simulate the dynamics of peatland area, calibrated by present-day wetland areas that are regularly inundated or subject to shallow water tables. The model is tested across a range of northern peatland sites and for gridded simulations over the Northern Hemisphere (> 30° N). Simulated northern peatland area (3.9 million km2), peat carbon stock (463 PgC) and peat depth are generally consistent with observed estimates of peatland area (3.4–4.0 million km2), peat carbon (270–540 PgC) and data compilations of peat core depths. Our results show that both net primary production (NPP) and heterotrophic respiration (HR) of northern peatlands increased over the past century in response to CO2 and climate change. NPP increased more rapidly than HR, and thus net ecosystem production (NEP) exhibited a positive trend, contributing a cumulative carbon storage of 11.13 Pg C since 1901, most of it being realized after the 1950s.

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Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

Author(s):

Mustafa Berk Duygu ◽

Zuhal Akyürek

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

High Temporal Resolution ◽

Land Surface Models ◽

Statistical Measures ◽

Surface Models ◽

Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

Geoscientific Model Development ◽

10.5194/gmd-7-361-2014 ◽

2014 ◽

Vol 7 (1) ◽

pp. 361-386 ◽

Cited By ~ 121

Author(s):

D. N. Walters ◽

K. D. Williams ◽

I. A. Boutle ◽

A. C. Bushell ◽

J. M. Edwards ◽

...

Keyword(s):

Land Surface ◽

Weather Prediction ◽

Regional Climate ◽

Land Surface Model ◽

Unified Model ◽

Surface Model ◽

Development Cycle ◽

Global Land ◽

Overall Performance ◽

Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

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Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-2555-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 2555-2608 ◽

Cited By ~ 2

Author(s):

E. H. Sutanudjaja ◽

L. P. H. van Beek ◽

S. M. de Jong ◽

F. C. van Geer ◽

M. F. P. Bierkens

Keyword(s):

Sensitivity Analysis ◽

Land Surface ◽

Large Scale ◽

Groundwater Modeling ◽

Land Surface Model ◽

Water Levels ◽

Surface Model ◽

Groundwater Model ◽

Model Output ◽

Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

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